A Shortcut from Categorical Quantum Theory to Convex Operational Theories

TL;DR

This paper establishes a direct connection between the categorical approach to quantum mechanics and convex operational theories, providing a framework to interpret physical systems and processes as ordered vector spaces.

Contribution

It introduces a functorial bridge from categorical quantum theory to convex operational models, clarifying how probabilistic and compositional structures relate.

Findings

Defines a covariant functor from symmetric monoidal categories to ordered vector spaces

Shows how natural transformations identify normalized states in the models

Demonstrates conditions under which the image category forms a symmetric monoidal structure

Abstract

This paper charts a very direct path between the categorical approach to quantum mechanics, due to Abramsky and Coecke, and the older convex-operational approach based on ordered vector spaces (recently reincarnated as "generalized probabilistic theories"). In the former, the objects of a symmetric monoidal category C are understood to represent physical systems and morphisms, physical processes. Elements of the monoid C(I,I) are interpreted somewhat metaphorically as probabilities. Any monoid homomorphism from the scalars of a symmetric monoidal category C gives rise to a covariant functor V_o from C to a category of dual-pairs of ordered vector spaces. Specifying a natural transformation u from V_o to 1 (where 1 is the trivial such functor) allows us to identify normalized states, and, thus, to regard the image category V_o(C) as consisting of concrete operational models. In this case, if A and B are objects in C, then V_o(A x B) defines a non-signaling composite of V_o(A) and V_o(B). Provided either that C satisfies a "local tomography" condition, or that C is compact closed, this defines a symmetric monoidal structure on the image category, and makes V_o a (strict) monoidal functor.